README.md

metadata

language:
  - en
license: mit
pipeline_tag: text-generation
tags:
  - analog-circuits
  - circuit-generation
  - transformer
  - generative-model

AnalogToBi

AnalogToBi is a generative framework for device-level analog circuit topology generation, introduced in the paper AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding.

The model generates valid and novel analog circuit topologies conditioned on a target circuit type using a Transformer decoder.

Key Features

• Circuit-type conditioning: Explicit functional control across 15 circuit categories (e.g., OpAmp, LDO, Comparator).
• Bipartite graph representation: Decouples devices and nets into distinct node types for better structural generalization.
• Grammar-guided decoding: State machine-based constrained decoding enforces electrical validity during generation.
• Device renaming augmentation: Randomizes device numbering to mitigate memorization and improve novelty.

Experimental results show that AnalogToBi achieves 97.8% validity and 92.1% novelty in generated circuits without human-in-the-loop training.

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Paper

AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding

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Code

Official implementation: https://github.com/Seungmin0825/AnalogToBi

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Usage

To generate circuit topologies using the grammar-guided decoder, you can use the following command from the official repository:

python GPT_Inference_Grammar.py CIRCUIT_Opamp

Citation

@article{kim2026analogtobi,
  title={AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding},
  author={Kim, Seungmin and Kim, Mingun and Lee, Yuna and Kim, Yulhwa},
  journal={arXiv preprint arXiv:2603.08720},
  year={2026}
}